The development of at least some of the presently disclosed subject matter may have been funded in part by the United States Government. The United States government may have rights in the invention.
The present invention is related to traffic monitoring systems, and more particularly to a traffic monitoring system for detecting, measuring and anticipating vehicle motion.
Systems for monitoring vehicular traffic are known. For example, it is known to detect vehicles by employing inductive loop sensors. At least one loop of wire or a similar conductive element may be disposed beneath the surface of a roadway at a predetermined location. Electromagnetic induction occurs when a vehicle occupies the roadway above the loop. The induction can be detected via a simple electronic circuit that is coupled with the loop. The inductive loop and associated detection circuitry can be coupled with an electronic counter circuit to count the number of vehicles that pass over the loop. However, inductive loops are subjected to harsh environmental conditions and consequently have a relatively short expected lifespan.
It is also known to employ optical sensors to monitor vehicular traffic. For example, traffic monitoring systems that employ xe2x80x9cmachine visionxe2x80x9d technology such as video cameras are known. Machine vision traffic monitoring systems are generally mounted above the surface of the roadway and have the potential for much longer lifespan than inductive loop systems. Further, machine vision traffic monitoring systems have the potential to provide more information about traffic conditions than inductive loop traffic monitoring systems. However, known machine vision traffic monitoring systems have not achieved these potentials.
In accordance with the present invention, a traffic monitoring station employs at least one video camera and a computation unit to detect and track vehicles passing through the field of view of the video camera. The camera provides a video image of a section of roadway in the form of successive individual video frames. Motion is detected through edge analysis and changes in luminance relative to an edge reference frame and a luminance reference frame. The frames are organized into a plurality of sets of pixels. Each set of pixels (xe2x80x9ctilexe2x80x9d) is in either an xe2x80x9cactivexe2x80x9d state or an xe2x80x9cinactivexe2x80x9d state. A tile becomes active when the luminance or edge values of the pixels of the tile differ from the luminance and edge values of the corresponding tiles in the corresponding reference frames in accordance with predetermined criteria. The tile becomes inactive when the luminance and edge values of the pixels of the tile do not differ from the corresponding reference frame tiles in accordance with the predetermined criteria.
The reference frames, which represent the view of the camera without moving vehicles, may be dynamically updated in response to conditions in the field of view of the camera. The reference frames are updated by combining each new frame with the respective reference frames. The combining calculation is weighted in favor of the reference frames to provide a gradual rate of change in the reference frames. A previous frame may also be employed in a xe2x80x9cframe-to-framexe2x80x9d comparison with the new frame to detect motion. The frame-to-frame comparison may provide improved results relative to use of the reference frame in conditions of low light and darkness.
Each object is represented by at least one group of proximate active tiles (xe2x80x9cquantaxe2x80x9d). Individual quantum, each of which contains a predetermined maximum number of tiles, are tracked through successive video frames. The distance travelled by each quantum is readily calculable from the change in position of the quantum relative to stationary features in the field of view of the camera. The time taken to travel the distance is readily calculable since the period of time between successive frames is known. Physical parameters such as velocity, acceleration and direction of travel of the quantum are calculated based on change in quantum position over time. Physical parameters that describe vehicle motion are calculated by employing the physical parameters calculated for the quanta. For example, the velocities calculated for the quanta that comprise the vehicle may be combined and averaged to ascertain the velocity of the vehicle.
The motion and shape of quanta are employed to delineate vehicles from other objects. A plurality of segmenter algorithms is employed to perform grouping, dividing and pattern matching functions on the quanta. For example, some segmenter algorithms employ pattern matching to facilitate identification of types of vehicles, such as passenger automobiles and trucks. A physical mapping of vehicle models may be employed to facilitate the proper segmentation of vehicles. A list of possible new objects is generated from the output of the segmenter algorithms. The list of possible new objects is compared with a master list of objects, and objects from the list of possible new objects that cannot be found in the master list are designated as new objects. The object master list is then updated by adding the new objects to the object master list.
At least one feature extractor is employed to generate a descriptive vector for each object. The descriptive vector is provided to a neural network classification engine which classifies and scores each object. The resultant score indicates the probability of the object being a vehicle of a particular type. Objects that produce a score that exceeds a predetermined threshold are determined to be vehicles.
The traffic monitoring station may be employed to facilitate traffic control in real time. Predetermined parameters that describe vehicle motion may be employed to anticipate future vehicle motion. Proactive action may then be taken to control traffic in response to the anticipated motion of the vehicle. For example, if on the basis of station determined values for vehicle distance from the intersection, speed, accelleration, and vehicle class (truck, car, etc.), the traffic monitoring station determines that the vehicle will xe2x80x9crun a red light,xe2x80x9d traversing an intersection during a period of time when the traffic signal will be otherwise be indicating xe2x80x9cgreenxe2x80x9d for vehicles entering the intersection from another direction, the traffic monitoring station can delay the green light for the other vehicles or cause some other actions to be taken to reduce the likelihood of a collision. Such actions may also include displaying the green light for the other vehicles in an altered mode (e.g., flashing) or in some combination with another signal light (e.g., yellow or red), or initiating an audible alarm at the intersection until the danger has passed. Further, the traffic monitoring station may track the offending vehicle through the intersection and record a full motion video movie of the event for vehicle identification and evidentiary purposes.